RESEARCH

Not All Memories are Created Equal: Learning to Forget by Expiring

May 14, 2021

Abstract

Attention mechanisms have shown promising results in sequence modeling tasks that require longterm memory. However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous timesteps efficiently, as not all states from previous timesteps are preserved. We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality. Next, we show that Expire-Span can scale to memories that are tens of thousands in size, setting a new state of the art on incredibly long context tasks such as character-level language modeling and a frame-by-frame moving objects task. Finally, we analyze the efficiency of Expire-Span compared to existing approaches and demonstrate that it trains faster and uses less memory.

Download the Paper

Related Publications

June 14, 2020

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA | Facebook AI Research

Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach

June 14, 2020

April 25, 2020

Permutation Equivariant Models for Compositional Generalization in Language | Facebook AI Research

Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt

April 25, 2020

September 15, 2019

SPEECH & AUDIO

Who Needs Words? Lexicon-Free Speech Recognition | Facebook AI Research

Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

September 15, 2019

September 10, 2019

NLP

Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling | Facebook AI Research

Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

September 10, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.